Publications

@Article{nu15071728, AUTHOR = {Karabay, Aknur and Bolatov, Arman and Varol, Huseyin Atakan and Chan, Mei-Yen}, TITLE = {A Central Asian Food Dataset for Personalized Dietary Interventions}, JOURNAL = {Nutrients}, VOLUME = {15}, YEAR = {2023}, NUMBER = {7}, ARTICLE-NUMBER = {1728}, URL = {https://www.mdpi.com/2072-6643/15/7/1728}, ISSN = {2072-6643}, ABSTRACT = {Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms. Leveraging these social trends, real-time food recognition and reliable classification of these captured food images can potentially help replace some of the tedious recording and coding of food diaries to enable personalized dietary interventions. Although Central Asian cuisine is culturally and historically distinct, there has been little published data on the food and dietary habits of people in this region. To fill this gap, we aim to create a reliable dataset of regional foods that is easily accessible to both public consumers and researchers. To the best of our knowledge, this is the first work on the creation of a Central Asian Food Dataset (CAFD). The final dataset contains 42 food categories and over 16,000 images of national dishes unique to this region. We achieved a classification accuracy of 88.70% (42 classes) on the CAFD using the ResNet152 neural network model. The food recognition models trained on the CAFD demonstrate the effectiveness and high accuracy of computer vision for dietary assessment.}, DOI = {10.3390/nu15071728} }

@INPROCEEDINGS{10066796, author={Kuzdeuov, Askat and Koishigarina, Darina and Varol, Huseyin Atakan}, booktitle={2023 IEEE International Conference on Big Data and Smart Computing (BigComp)}, title={AnyFace: A Data-Centric Approach For Input-Agnostic Face Detection}, year={2023}, volume={}, number={}, pages={211-218}, doi={10.1109/BigComp57234.2023.00042}}

@INPROCEEDINGS{10066768, author={Orel, Daniil and Yeshpanov, Rustem and Varol, Huseyin Atakan}, booktitle={2023 IEEE International Conference on Big Data and Smart Computing (BigComp)}, title={Speech Recognition for Turkic Languages Using Cross-Lingual Transfer Learning from Kazakh}, year={2023}, volume={}, number={}, pages={174-182}, doi={10.1109/BigComp57234.2023.00037}}

@EMBC 2023, author={Batyr Arystanbekov, Askat Kuzdeuov, Shakhizat Nurgaliyev, and Huseyin Atakan Varol}, booktitle={2023 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, title={Image Captioning for Visually Impaired and Blind: A Recipe for Lower-Resourced Languages}, year={2023}, volume={}, number={}, pages={}, doi={10.36227/techrxiv.22133894.v2}

@inproceedings {yeshpanov23_interspeech, author={Rustem Yeshpanov and Saida Mussakhojayeva and Yerbolat Khassanov}, title={{Multilingual Text-to-Speech Synthesis for Turkic Languages Using Transliteration}}, year=2023, booktitle={Proc. INTERSPEECH 2023}, pages={5521--5525}, doi={10.21437/Interspeech.2023-249} }

@ARTICLE{10246330, author={Makhataeva, Zhanat and Akhmetov, Tolegen and Varol, Huseyin Atakan}, journal={IEEE Transactions on Human-Machine Systems}, title={Augmented-Reality-Based Human Memory Enhancement Using Artificial Intelligence}, year={2023}, volume={}, number={}, pages={1-13}, doi={10.1109/THMS.2023.3307397}}

@INPROCEEDINGS{10312708, author={Orel, Daniil and Varol, Huseyin Atakan}, booktitle={IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society}, title={Noise-Robust Automatic Speech Recognition for Industrial and Urban Environments}, year={2023}, volume={}, number={}, pages={1-6}, doi={10.1109/IECON51785.2023.10312708}}

@INPROCEEDINGS{10311869, author={Balgabekova, Zarema and Alaran, Muslim and Varol, Huseyin Atakan}, booktitle={IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society}, title={A Data-Centric Approach for Object Recognition in Hemispherical Camera Images}, year={2023}, volume={}, number={}, pages={1-6}, doi={10.1109/IECON51785.2023.10311869}}

@ARTICLE{10343164, author={Shakerimov, Aidar and Alizadeh, Tohid and Varol, Huseyin Atakan}, journal={IEEE Access}, title={Efficient Sim-to-Real Transfer in Reinforcement Learning Through Domain Randomization and Domain Adaptation}, year={2023}, volume={11}, number={}, pages={136809-136824}, doi={10.1109/ACCESS.2023.3339568}}

@ARTICLE{10374245, author={Abdrakhmanova, Madina and Unaspekov, Timur and Varol, Huseyin Atakan}, journal={IEEE Transactions on Biometrics, Behavior, and Identity Science}, title={Multimodal Person Verification With Generative Thermal Data Augmentation}, year={2023}, volume={}, number={}, pages={1-1}, doi={10.1109/TBIOM.2023.3346938}}

@INPROCEEDINGS{10601292, author={Kuzdeuov, Askat and Nurgaliyev, Shakhizat and Turmakhan, Diana and Laiyk, Nurkhan and Varol, Huseyin Atakan}, booktitle={2023 3rd International Conference on Robotics, Automation and Artificial Intelligence (RAAI)}, title={Speech Command Recognition: Text-to-Speech and Speech Corpus Scraping Are All You Need}, year={2023}, volume={}, number={}, pages={286-291}, keywords={Accuracy;Speech coding;Virtual assistants;Speech recognition;Data collection;Benchmark testing;Data models;Speech commands recognition;text-to-speech;Kazakh Speech Corpus;voice commands;data-centric AI}, doi={10.1109/RAAI59955.2023.10601292}}

@INPROCEEDINGS{10601208, author={Akhmetov, Tolegen and Moger, Gourav and Tursynbek, Iliyas and Atakan Varol, Huseyin}, booktitle={2023 3rd International Conference on Robotics, Automation and Artificial Intelligence (RAAI)}, title={Thermal Perception Using Augmented Reality for Industrial Safety}, year={2023}, volume={}, number={}, pages={91-96}, keywords={Wireless communication;Industries;Visualization;Three-dimensional displays;Thermal factors;Service robots;Cameras;augmented reality;thermal camera;industrial safety;computer vision}, doi={10.1109/RAAI59955.2023.10601208}}

@inproceedings{abdrakhmanova22_odyssey, author={Madina Abdrakhmanova and Saniya Abushakimova and Yerbolat Khassanov and Huseyin Atakan Varol}, title={{A Study of Multimodal Person Verification Using Audio-Visual-Thermal Data}}, year=2022, booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2022)}, pages={233--239}, doi={10.21437/Odyssey.2022-33} }

@ARTICLE{9781417, author={Kuzdeuov, Askat and Aubakirova, Dana and Koishigarina, Darina and Varol, Huseyin Atakan}, journal={IEEE Transactions on Information Forensics and Security}, title={TFW: Annotated Thermal Faces in the Wild Dataset}, year={2022}, volume={17}, number={}, pages={2084-2094}, doi={10.1109/TIFS.2022.3177949}}

@InProceedings{yeshpanov-khassanov-varol:2022:LREC, author = {Yeshpanov, Rustem and Khassanov, Yerbolat and Varol, Huseyin Atakan}, title = {KazNERD: Kazakh Named Entity Recognition Dataset}, booktitle = {Proceedings of the Language Resources and Evaluation Conference}, month = {June}, year = {2022}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {417--426}, abstract = {We present the development of a dataset for Kazakh named entity recognition. The dataset was built as there is a clear need for publicly available annotated corpora in Kazakh, as well as annotation guidelines containing straightforward—but rigorous—rules and examples. The dataset annotation, based on the IOB2 scheme, was carried out on television news text by two native Kazakh speakers under the supervision of the first author. The resulting dataset contains 112,702 sentences and 136,333 annotations for 25 entity classes. State-of-the-art machine learning models to automatise Kazakh named entity recognition were also built, with the best-performing model achieving an exact match F1-score of 97.22\% on the test set. The annotated dataset, guidelines, and codes used to train the models are freely available for download under the CC BY 4.0 licence from https://github.com/IS2AI/KazNERD.}, url = {https://aclanthology.org/2022.lrec-1.44} }

@InProceedings{mussakhojayeva-khassanov-varol:2022:LREC, author = {Mussakhojayeva, Saida and Khassanov, Yerbolat and Varol, Huseyin Atakan}, title = {KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics}, booktitle = {Proceedings of the Language Resources and Evaluation Conference}, month = {June}, year = {2022}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {5404--5411}, abstract = {We present an expanded version of our previously released Kazakh text-to-speech (KazakhTTS) synthesis corpus. In the new KazakhTTS2 corpus, the overall size has increased from 93 hours to 271 hours, the number of speakers has risen from two to five (three females and two males), and the topic coverage has been diversified with the help of new sources, including a book and Wikipedia articles. This corpus is necessary for building high-quality TTS systems for Kazakh, a Central Asian agglutinative language from the Turkic family, which presents several linguistic challenges. We describe the corpus construction process and provide the details of the training and evaluation procedures for the TTS system. Our experimental results indicate that the constructed corpus is sufficient to build robust TTS models for real-world applications, with a subjective mean opinion score ranging from 3.6 to 4.2 for all the five speakers. We believe that our corpus will facilitate speech and language research for Kazakh and other Turkic languages, which are widely considered to be low-resource due to the limited availability of free linguistic data. The constructed corpus, code, and pretrained models are publicly available in our GitHub repository.}, url = {https://aclanthology.org/2022.lrec-1.578} }

@INPROCEEDINGS{9708901, author={Kuzdeuov, Askat and Koishigarina, Darina and Aubakirova, Dana and Abushakimova, Saniya and Varol, Huseyin Atakan}, booktitle={2022 IEEE/SICE International Symposium on System Integration (SII)}, title={SF-TL54: A Thermal Facial Landmark Dataset with Visual Pairs}, year={2022}, volume={}, number={}, pages={748-753}, doi={10.1109/SII52469.2022.9708901}}

@Article{s22186807, AUTHOR = {Mukashev, Dinmukhammed and Zhuzbay, Nurdaulet and Koshkinbayeva, Ainur and Orazbayev, Bakhtiyar and Kappassov, Zhanat}, TITLE = {PhotoElasticFinger: Robot Tactile Fingertip Based on Photoelastic Effect}, JOURNAL = {Sensors}, VOLUME = {22}, YEAR = {2022}, NUMBER = {18}, ARTICLE-NUMBER = {6807}, URL = {https://www.mdpi.com/1424-8220/22/18/6807}, ISSN = {1424-8220}, ABSTRACT = {The sense of touch is fundamental for a one-to-one mapping between the environment and a robot that physically interacts with the environment. Herein, we describe a tactile fingertip design that can robustly detect interaction forces given data collected from a camera. This design is based on the photoelastic effect observed in silicone matter. Under the force applied to the silicone rubber, owing to the stress-induced birefringence, the light propagating within the silicone rubber is subjected to the angular phase shift, where the latter is proportional to the increase in the image brightness in the camera frames. We present the calibration and test results of the photoelastic sensor design on a bench using a robot arm and with a certified industrial force torque sensor. We also discuss the applications of this sensor design and its potential relationship with human mechano-transduction receptors. We achieved a force sensing range of up to 8 N with a force resolution of around 0.5 N. The photoelastic tactile fingertip is suitable for robot grasping and might lead to further progress in robust tactile sensing.}, DOI = {10.3390/s22186807} }

@ARTICLE{9925633, author={Akhmetov, Tolegen and Varol, Huseyin Atakan}, journal={IEEE Transactions on Industrial Informatics}, title={An Augmented Reality-Based Warning System for Enhanced Safety in Industrial Settings}, year={2022}, volume={}, number={}, pages={1-12}, doi={10.1109/TII.2022.3216009}}